Under the MNAR assumption of missing data, MI results rely on subtle, untestable assumptions, and may depend heavily on the particular way the missingness mechanism is modelled. This issue emphasises the central role of sensitivity analysis, which explores how inference may vary under different missingness mechanisms.[1]} MI offers flexibility for sensitivity analysis, since the imputation model can be tuned to incorporate possible departures from the MAR assumption.[1]}, [3]} Unfortunately, a sensitivity analysis is often not performed or reported sufficiently in practice,[4]}, [5]} a tendency abetted by the practical constraints of many applied projects. When the population-level information about the incomplete covariate is available, our proposed calibrated-\(\delta \)  adjustment MI method provides a useful tool for performing a single, calibrated sensitivity analysis to assess the impact of potential departures from the MAR assumption.
